Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models
As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2403.15952.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-03T21:44:01.631528Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-01T22:26:18.101110Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation cd6fbf9e-f7c2-4509-b98a-77fc80dd3366 · inbound
MVI-Bench: A Comprehensive Benchmark for Evaluating Robustness to Misleading Visual Inputs in LVLMs cites this paper.
MVI-Bench: A Comprehensive Benchmark for Evaluating Robustness to Misleading Visual Inputs in LVLMs IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models
Reference 43
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T19:54:20.276464Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 75a71166-75b6-4c34-9aa1-47523f66e0fe · inbound
MVI-Bench: A Comprehensive Benchmark for Evaluating Robustness to Misleading Visual Inputs in LVLMs cites this paper.
MVI-Bench: A Comprehensive Benchmark for Evaluating Robustness to Misleading Visual Inputs in LVLMs IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models
Reference 43
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T21:44:01.631528Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation aa3009b2-a25a-4be7-b765-98b5cd75a494 · inbound
SMSP: A Plug-and-Play Strategy of Multi-Scale Perception for MLLMs to Perceive Visual Illusions cites this paper.
SMSP: A Plug-and-Play Strategy of Multi-Scale Perception for MLLMs to Perceive Visual Illusions IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models
Reference 26
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T17:37:24.238320Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c470afb2-a3ab-46ec-9af8-360ac8356a35 · inbound
VLMs Need Words: Vision Language Models Ignore Visual Detail In Favor of Semantic Anchors cites this paper.
VLMs Need Words: Vision Language Models Ignore Visual Detail In Favor of Semantic Anchors IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models
Reference 15
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T21:58:19.876482Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 72376066-0eef-47af-975c-16ec9ffa031e · inbound
Saliency-R1: Enforcing Interpretable and Faithful Vision-language Reasoning via Saliency-map Alignment Reward cites this paper.
Saliency-R1: Enforcing Interpretable and Faithful Vision-language Reasoning via Saliency-map Alignment Reward IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models
Reference 58
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T22:20:47.820906Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 560d5947-15b0-46f1-b2fa-12aee1f6899f · inbound
Illusion-Aware Visual Preprocessing and Anti-Illusion Prompting for Classic Illusion Understanding in Vision-Language Models cites this paper.
Illusion-Aware Visual Preprocessing and Anti-Illusion Prompting for Classic Illusion Understanding in Vision-Language Models IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models
Reference 17
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T01:46:14.568503Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation b8763a95-16c8-40d0-8524-5248b3dbd631 · inbound
Readable Yet Unpredictable: Rotated-Outcome Prediction in Vision-Language Models cites this paper.
Readable Yet Unpredictable: Rotated-Outcome Prediction in Vision-Language Models IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models
Reference 6
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-01T22:26:18.102419Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.